In this work, we compare our proposal against four recently published, publicly available, deep learning-based reconstruction methods [1]}, [2]}, [3]}, [4]}. These approaches were evaluated at \(75\%\)  and \(80\%\)  undersampling levels (corresponding to acquisition acceleration factors of \(4\times \)  and \(5\times \) ). The experiments were done using MR raw-data acquired from subjects scanned using a volumetric \(T1\) -weighted MR imaging sequence. The proposed reconstruction code has been made publicly available at https://github.com/rmsouza01/Hybrid-CS-Model-MRI. The dataset will be made publicly available for benchmarking purposes at https://sites.google.com/view/calgary-campinas-dataset/home and is part of the Calgary-Campinas datasets [5]}.
